{
 "cells": [
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   "cell_type": "markdown",
   "id": "8093e27a-33f6-4cd9-a47b-ea94c3d0c514",
   "metadata": {},
   "source": [
    "\n",
    "<h1><center>Huggingface with ART</center></h1>\n",
    "\n",
    "\n",
    "In this notebook we will go over how to use the Huggingface API with ART. This can enable us to train robust foundation models which act over images. \n",
    "\n",
    "Currently this is a developing feature, and so not all ART tools are supported. Further tools and development is planned. As of ART 1.16 we support: \n",
    "+ Using a Pytorch backend.\n",
    "+ Evasion attacks and defences on classical classification tasks such as image classification, but not tasks such as object detection.\n",
    "\n",
    "If you have a use case that is not supported (or find a bug in this new feature!) please raise an issiue on ART.\n",
    "\n",
    "In addition to the core ART dependancies you will need to install Pytorch and the Transformers library:\n",
    "\n",
    "`pip install transformers`\n",
    "\n",
    "`pip3 install torch torchvision`\n",
    "\n",
    "This notebook was tested with the transformers library version 4.30.2, and torch==2.1.0/torchvision==0.16.0\n",
    "\n",
    "Let's look at how we can use ART to secure Huggingface models!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "549129e5-f6f4-4995-b334-d22cb4cae76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Relevant imports for the notebook\n",
    "\n",
    "import transformers\n",
    "import torch\n",
    "from torch.optim import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "from torchvision import datasets\n",
    "from art.estimators.classification.hugging_face import HuggingFaceClassifierPyTorch\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0adf2bba-efc1-4db7-8041-698105537b08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use CIFAR data for the notebook.\n",
    "def get_cifar_data(fetch_subset=False):\n",
    "    \"\"\"\n",
    "    Get CIFAR-10 data.\n",
    "    :return: cifar train/test data.\n",
    "    \"\"\"\n",
    "    train_set = datasets.CIFAR10('./data', train=True, download=True)\n",
    "    test_set = datasets.CIFAR10('./data', train=False, download=True)\n",
    "\n",
    "    x_train = train_set.data.astype(np.float32)\n",
    "    y_train = np.asarray(train_set.targets)\n",
    "\n",
    "    x_test = test_set.data.astype(np.float32)\n",
    "    y_test = np.asarray(test_set.targets)\n",
    "\n",
    "    x_train = np.moveaxis(x_train, [3], [1])\n",
    "    x_test = np.moveaxis(x_test, [3], [1])\n",
    "\n",
    "    x_train = x_train / 255.0\n",
    "    x_test = x_test / 255.0\n",
    "    \n",
    "    if fetch_subset:  \n",
    "        return (x_train[0:2500], y_train[0:2500]), (x_test[0:2500], y_test[0:2500])\n",
    "    \n",
    "    return (x_train, y_train), (x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b5ebcb",
   "metadata": {},
   "source": [
    "# Regular Training with ART\n",
    "\n",
    "We will first see how to load a model into ART's HuggingFaceClassifierPyTorch, train it, and attack it with PGD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f797586-480b-432e-8b55-26a4ac2360f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_base_model(architecture='google/vit-base-patch16-224', train_on_subset=False):\n",
    "    \"\"\"\n",
    "    Train a cifar classifier\n",
    "    \"\"\"\n",
    "\n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data(train_on_subset)\n",
    "\n",
    "    # Here we load a Huggingface model using the transformers library.\n",
    "    model = transformers.AutoModelForImageClassification.from_pretrained(architecture,\n",
    "                                                                         ignore_mismatched_sizes=True,\n",
    "                                                                         num_labels=10)\n",
    "    model = model.to(device)\n",
    "    # The HuggingFaceClassifierPyTorch follows broadly the same API as the PyTorchClassifier\n",
    "    # So we can supply the loss function, the input shape of the data we will supply, the optimizer, etc.\n",
    "    # Note, frequently we will be performing fine-tuning or transfer learning with vision transformners and \n",
    "    # so we may be fine-tuning on differently sized inputs. \n",
    "    # The input_shape argument refers to the shape of the supplied input data which may be different to \n",
    "    # the shape required by the model. \n",
    "    # To handle this HuggingFaceClassifierPyTorch has an extra argument of processor which will act on \n",
    "    # every batch to process the data into the correct form required by the supplied model.\n",
    "    # This needs to be manually specified by the user. For many attacks and defences to work it needs to be a \n",
    "    # differentiable funciton. \n",
    "    # Here the processor is a simple upsampler to enlarge the cifar images into the right size.\n",
    "    upsampler = torch.nn.Upsample(scale_factor=7, mode='nearest')\n",
    "\n",
    "    optimizer = Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "    hf_model = HuggingFaceClassifierPyTorch(model, \n",
    "                                            loss=torch.nn.CrossEntropyLoss(),\n",
    "                                            optimizer=optimizer,\n",
    "                                            input_shape=(3, 32, 32),\n",
    "                                            nb_classes=10,\n",
    "                                            clip_values=(0, 1),\n",
    "                                            processor=upsampler)\n",
    "    hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n",
    "    return hf_model"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "01f68d34-e777-48b4-bdca-7c52a63e6e50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n",
      "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([10]) in the model instantiated\n",
      "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
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      "text/plain": [
       "Epochs:   0%|          | 0/2 [00:00<?, ?it/s]"
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   "source": [
    "# Change to train_on_subset=True and use for architecture='WinKawaks/vit-tiny-patch16-224' quicker results.\n",
    "# The model will have lower performance than the pre-ran notebook, but it \n",
    "# will run the notbook much faster.\n",
    "\n",
    "model_to_examine='google/vit-base-patch16-224'\n",
    "train_on_subset=False\n",
    "\n",
    "# Estimated run time: approx. 10 min on a laptop CPU\n",
    "# model_to_examine='WinKawaks/vit-tiny-patch16-224'\n",
    "# train_on_subset=True\n",
    "\n",
    "hf_base_model = train_base_model(architecture=model_to_examine, train_on_subset=train_on_subset)\n",
    "torch.save(hf_base_model.model.state_dict(), 'hf_base_model.pt')\n",
    "del hf_base_model # Clear models we no longer need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a370096-d145-42cf-a59e-c553d81b0967",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_pgd(architecture, model_to_test='hf_base_model.pt'):\n",
    "    \"\"\"\n",
    "    Here we can test the model we trained against a PGD attack.\n",
    "    \"\"\"\n",
    "    \n",
    "    import os\n",
    "    from art.attacks.evasion import ProjectedGradientDescentPyTorch\n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data()\n",
    "    model = transformers.AutoModelForImageClassification.from_pretrained(architecture,\n",
    "                                                                         ignore_mismatched_sizes=True,\n",
    "                                                                         num_labels=10)\n",
    "    \n",
    "    # Load the model state dict from the training loop we just performed.\n",
    "    model.load_state_dict(torch.load(model_to_test, map_location=torch.device(device)))\n",
    "    optimizer = Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "    # Set it up as a HuggingFaceClassifierPyTorch\n",
    "    hf_model = HuggingFaceClassifierPyTorch(model, \n",
    "                                            loss=torch.nn.CrossEntropyLoss(),\n",
    "                                            optimizer=optimizer,\n",
    "                                            input_shape=(3, 32, 32),\n",
    "                                            nb_classes=10,\n",
    "                                            clip_values=(0, 1),\n",
    "                                            processor=torch.nn.Upsample(scale_factor=7, mode='nearest'))\n",
    "\n",
    "    # Let's just use 100 samples for quick demo purposes\n",
    "    num_samples = 100\n",
    "    outputs = hf_model.predict(x_test[:num_samples])\n",
    "    acc = np.sum(np.argmax(outputs, axis=1) == y_test[:num_samples]) / len(y_test[:num_samples])\n",
    "    print('clean acc ', acc)\n",
    "\n",
    "    # The backend of the HuggingFaceClassifierPyTorch is the existing PyTorchClassifier. \n",
    "    # Thus we can interface HuggingFaceClassifierPyTorch with  aleadry existing attacks in ART which support pytorch.\n",
    "    # Here we use ProjectedGradientDescentPyTorch.\n",
    "    attacker = ProjectedGradientDescentPyTorch(hf_model, eps=8/255, eps_step=1/255)\n",
    "    x_test_adv_robust = attacker.generate(x_test[:num_samples])\n",
    "    outputs = hf_model.predict(x_test_adv_robust)\n",
    "    acc = np.sum(np.argmax(outputs, axis=1) == y_test[:num_samples]) / len(y_test[:num_samples])\n",
    "    print('adv acc ', acc)\n",
    "\n",
    "    # We can display the adversarial examples to highlight the added perturbation to the original sample.\n",
    "    x_test_adv_robust = np.moveaxis(x_test_adv_robust, [1], [3])\n",
    "    x_test = np.moveaxis(x_test, [1], [3])\n",
    "\n",
    "    delta = ((x_test[:num_samples] - x_test_adv_robust) + 8/255) * 10 # shift to have min 0 and make perturbations 10x larger to visualise them.\n",
    "\n",
    "    fig, axs = plt.subplots(3, 3)\n",
    "    for i in range(3):\n",
    "        axs[i, 0].imshow(x_test_adv_robust[i])\n",
    "        axs[i, 1].imshow(x_test[i])\n",
    "        axs[i, 2].imshow(delta[i])\n",
    "    plt.tight_layout()\n",
    "    del hf_model # clear memory of unneeded models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f4625ff-103f-49d2-90f9-503f1ea43e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n",
      "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([10]) in the model instantiated\n",
      "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean acc  0.98\n"
     ]
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PGD - Batches:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv acc  0.01\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pgd(architecture=model_to_examine, model_to_test='hf_base_model.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bc9c9df",
   "metadata": {},
   "source": [
    "# Adversarial Training with ART\n",
    "\n",
    "Now, rather than using regular training, we employ robust PGD training and evaluate the robust model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa81f918-e716-457d-a665-835542cf5d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that we can attack the Huggingface transformer, so now let's use one of the defences in ART!\n",
    "\n",
    "def adversarial_train(model_to_examine):\n",
    "    from art.defences.trainer import AdversarialTrainerMadryPGD\n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data()\n",
    "    model = transformers.AutoModelForImageClassification.from_pretrained(model_to_examine,\n",
    "                                                                         ignore_mismatched_sizes=True,\n",
    "                                                                         num_labels=10)\n",
    "\n",
    "    upsampler = torch.nn.Upsample(scale_factor=7, mode='nearest')\n",
    "\n",
    "    optimizer = Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "    hf_model = HuggingFaceClassifierPyTorch(model, \n",
    "                                            loss=torch.nn.CrossEntropyLoss(), \n",
    "                                            input_shape=(3, 32, 32),\n",
    "                                            nb_classes=10,\n",
    "                                            optimizer=optimizer,\n",
    "                                            clip_values=(0, 1),\n",
    "                                            processor=upsampler)\n",
    "\n",
    "    # We can now use adversarial training with Madry's protocol.\n",
    "    trainer = AdversarialTrainerMadryPGD(hf_model,\n",
    "                                         nb_epochs=10,\n",
    "                                         eps=8/255,\n",
    "                                         eps_step=1/255,\n",
    "                                         max_iter=10)\n",
    "\n",
    "    trainer.fit(x_train, y_train)\n",
    "    torch.save(trainer._classifier.model.state_dict(), 'hf_adv_model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76373745-f886-4f1f-9452-ec648753b04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the below to run the adverarial training, it can take some time depending on available hardware. \n",
    "# The expected runtime is around 15 hours using a Nvidia V100 GPU. More training could be conducted if better performance is desired.\n",
    "\n",
    "# adversarial_train(model_to_examine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51c859e2-c18a-4598-999b-b7621772a2d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224 and are newly initialized because the shapes did not match:\n",
      "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([10]) in the model instantiated\n",
      "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean acc  0.88\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PGD - Batches:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv acc  0.43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We now test the adversariallty trained model and we can see we have done from 0% robustness to 43%.\n",
    "test_pgd(architecture=model_to_examine, model_to_test='hf_adv_model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfeead39-4b74-4b60-bd4b-a933fb7eebc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of SwinForImageClassification were not initialized from the model checkpoint at microsoft/swin-tiny-patch4-window7-224 and are newly initialized because the shapes did not match:\n",
      "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([10]) in the model instantiated\n",
      "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1e1c1da709f43279594cebafaf48077",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae2f70627dd74fc19aa21b66f060d248",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/391 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d7352985ba345549ecdccfb8645382b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/391 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also try with different models, for example here we try the functions with a different architecture\n",
    "\n",
    "# ETA is around 25 min with a laptop CPU when using train_on_subset=True\n",
    "# this and the following cell can be skipped if the duration is too long.\n",
    "# train_on_subset=True\n",
    "\n",
    "hf_base_model = train_base_model(architecture='microsoft/swin-tiny-patch4-window7-224', \n",
    "                                 train_on_subset=train_on_subset)\n",
    "torch.save(hf_base_model.model.state_dict(), 'swin_tiny_base_model.pt')\n",
    "del hf_base_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86b1f346-6c7e-4546-ae10-becbdd442b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of SwinForImageClassification were not initialized from the model checkpoint at microsoft/swin-tiny-patch4-window7-224 and are newly initialized because the shapes did not match:\n",
      "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([10]) in the model instantiated\n",
      "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([10, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean acc  0.92\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PGD - Batches:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv acc  0.06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pgd(architecture='microsoft/swin-tiny-patch4-window7-224', \n",
    "         model_to_test='./swin_tiny_base_model.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9a80e5",
   "metadata": {},
   "source": [
    "# Training and Defending timm models\n",
    "\n",
    "PyTorch Image Models (timm) is a poular repository for SOTA implementations of image models and Huggingface \n",
    "is hosting many of the models and weights. \n",
    "\n",
    "We can use timm models here with the same wrapper. \n",
    "\n",
    "To run this part of the notebook we need to install the timm library\n",
    "\n",
    "`pip install timm`\n",
    "\n",
    "This notebook was ran with timm==0.9.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d6ccae2-4f26-402f-be21-bc3003f5a59a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also try different architectures, \n",
    "# for example one of the most popular models on Huggingface is the resnet-50\n",
    "def train_using_timm_model(train_on_subset, model_type):\n",
    "    import timm\n",
    "    \n",
    "    model = timm.create_model(model_type, pretrained=True)\n",
    "    model = model.to(device)\n",
    "    upsampler = torch.nn.Upsample(scale_factor=7, mode='nearest')\n",
    "    \n",
    "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
    "    \n",
    "    hf_model = HuggingFaceClassifierPyTorch(model, \n",
    "                                            loss=torch.nn.CrossEntropyLoss(), \n",
    "                                            input_shape=(3, 32, 32),\n",
    "                                            nb_classes=10,\n",
    "                                            optimizer=optimizer,\n",
    "                                            clip_values=(0, 1),\n",
    "                                            processor=upsampler)\n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data(train_on_subset)\n",
    "    hf_model.fit(x_train, y_train, nb_epochs=2, verbose=True)\n",
    "\n",
    "    return hf_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6584e1db-bc18-453d-9c8b-a313d1c9267e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eceb751607a642da8c06b35bb2ea1aba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epochs:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2913e0abb004bbd98852fea8eb8018b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/391 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8a3111477114196aaa2c250bb3dd9a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/391 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_on_subset=False\n",
    "model_type='resnet50.a1_in1k'\n",
    "\n",
    "# Toggle subset training for quicker results, although model performance is significantly lower.\n",
    "# ETA is around 10 min when training on a laptop CPU\n",
    "# train_on_subset=True\n",
    "# model_type='timm/resnet18.a1_in1k'\n",
    "\n",
    "hf_timm_model = train_using_timm_model(train_on_subset, model_type)\n",
    "torch.save(hf_timm_model.model.state_dict(), 'timm_resnet.pt')\n",
    "del hf_timm_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dff3de85-965a-45fe-9870-7858e6527d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adversarial_train_timm(model_type):\n",
    "    from art.defences.trainer import AdversarialTrainerMadryPGD\n",
    "    import timm\n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data()\n",
    "\n",
    "    model = timm.create_model(model_type, pretrained=True)\n",
    "\n",
    "    upsampler = torch.nn.Upsample(scale_factor=7, mode='nearest')\n",
    "\n",
    "    optimizer = Adam(model.parameters(), lr=1e-4)\n",
    "\n",
    "    hf_model = HuggingFaceClassifierPyTorch(model, \n",
    "                                            loss=torch.nn.CrossEntropyLoss(), \n",
    "                                            input_shape=(3, 32, 32),\n",
    "                                            nb_classes=10,\n",
    "                                            optimizer=optimizer,\n",
    "                                            clip_values=(0, 1),\n",
    "                                            processor=upsampler)\n",
    "\n",
    "    # We can now use adversarial training with Madry's protocol.\n",
    "    trainer = AdversarialTrainerMadryPGD(hf_model,\n",
    "                                         nb_epochs=10,\n",
    "                                         eps=8/255,\n",
    "                                         eps_step=1/255,\n",
    "                                         max_iter=10)\n",
    "\n",
    "    trainer.fit(x_train, y_train)\n",
    "    torch.save(trainer._classifier.model.state_dict(), 'timm_resnet_adv_model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "122ba410-7352-494b-8029-c747e5378855",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the below to run the adverarial training, it can take some time depending on available hardware. \n",
    "# The expected runtime is around 10 hours using a Nvidia V100 GPU. More training could be conducted if better performance is desired.\n",
    "\n",
    "# adversarial_train_timm(model_type=model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8a93fba-3d05-4316-91e6-de54869c22fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_on_pgd_timm(model_to_load, model_type):\n",
    "    from art.attacks.evasion import ProjectedGradientDescentPyTorch\n",
    "    import timm\n",
    "\n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data()\n",
    "    \n",
    "    model = timm.create_model(model_type, pretrained=True)\n",
    "\n",
    "    model.load_state_dict(torch.load(model_to_load, map_location=torch.device(device)))\n",
    "    loss_fn = torch.nn.CrossEntropyLoss()\n",
    "    from torch.optim import Adam\n",
    "    optimizer = Adam(model.parameters(), lr=1e-4)\n",
    "    hf_model = HuggingFaceClassifierPyTorch(model, \n",
    "                                            loss=torch.nn.CrossEntropyLoss(),\n",
    "                                            optimizer=optimizer,\n",
    "                                            input_shape=(3, 32, 32),\n",
    "                                            nb_classes=10,\n",
    "                                            clip_values=(0, 1),\n",
    "                                            processor=torch.nn.Upsample(scale_factor=7, mode='nearest'))\n",
    "\n",
    "    outputs = hf_model.predict(x_test[:100])\n",
    "    acc = np.sum(np.argmax(outputs, axis=1) == y_test[:100]) / len(y_test[:100])\n",
    "    print('clean acc ', acc)\n",
    "\n",
    "    attacker = ProjectedGradientDescentPyTorch(hf_model, eps=8/255, eps_step=1/255)\n",
    "    x_test_adv_robust = attacker.generate(x_test[:100])\n",
    "    outputs = hf_model.predict(x_test_adv_robust)\n",
    "    acc = np.sum(np.argmax(outputs, axis=1) == y_test[:100]) / len(y_test[:100])\n",
    "    print('adv acc ', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1ad911ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "clean acc  0.89\n"
     ]
    },
    {
     "data": {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "PGD - Batches:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv acc  0.08\n"
     ]
    }
   ],
   "source": [
    "test_on_pgd_timm(model_to_load='timm_resnet.pt', model_type=model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9fee39b0-5be1-4bb1-978d-b982f0642881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "clean acc  0.7\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adv acc  0.47\n"
     ]
    }
   ],
   "source": [
    "test_on_pgd_timm(model_to_load='timm_resnet_adv_model.pt', model_type=model_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d29b5ab-7f5b-4136-903e-fc0442c4f15b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also use the interface so supply custom Huggingface models. \n",
    "# Here is a simple example for running a toy neural network classifier.\n",
    "\n",
    "import torch\n",
    "from transformers.modeling_utils import PreTrainedModel\n",
    "from transformers.configuration_utils import PretrainedConfig\n",
    "from transformers.modeling_outputs import ImageClassifierOutput\n",
    "from art.estimators.classification.hugging_face import HuggingFaceClassifierPyTorch\n",
    "\n",
    "class ModelConfig(PretrainedConfig):\n",
    "    def __init__(\n",
    "            self,\n",
    "            **kwargs,\n",
    "    ):\n",
    "        super().__init__(**kwargs)\n",
    "        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "class Model(PreTrainedModel):\n",
    "\n",
    "    def __init__(self, config):\n",
    "        super().__init__(config)\n",
    "\n",
    "        self.conv = torch.nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)\n",
    "        self.conv2 = torch.nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3)\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        self.pool = torch.nn.MaxPool2d(2, 2)\n",
    "        self.fullyconnected = torch.nn.Linear(6272, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward function to evaluate the model\n",
    "        :param x: Input to the model\n",
    "        :return: Prediction of the model\n",
    "        \"\"\"\n",
    "        x = self.relu(self.conv(x))\n",
    "        x = self.relu(self.conv2(x))\n",
    "        x = self.pool(x)\n",
    "        x = x.reshape(-1, 6272)\n",
    "        x = self.fullyconnected(x)\n",
    "        return ImageClassifierOutput(logits=x)\n",
    "\n",
    "def train_simple_custom__model():\n",
    "    config = ModelConfig()\n",
    "    pt_model = Model(config=config)\n",
    "\n",
    "    optimizer = torch.optim.Adam(pt_model.parameters(), lr=1e-3)\n",
    "    \n",
    "    simple_hf_classifier = HuggingFaceClassifierPyTorch(pt_model,\n",
    "                                                         loss=torch.nn.CrossEntropyLoss(),\n",
    "                                                         optimizer=optimizer,\n",
    "                                                         input_shape=(3, 32, 32),\n",
    "                                                         nb_classes=10,\n",
    "                                                         clip_values=(0, 1),\n",
    "                                                         processor=None) # No processor is needed as the data is of the correct size for the model.\n",
    "    \n",
    "    (x_train, y_train), (x_test, y_test) = get_cifar_data()\n",
    "    simple_hf_classifier.fit(x_train, y_train, nb_epochs=20, verbose=True)\n",
    "\n",
    "    num_samples = 100\n",
    "    outputs = simple_hf_classifier.predict(x_test[:num_samples])\n",
    "    acc = np.sum(np.argmax(outputs, axis=1) == y_test[:num_samples]) / len(y_test[:num_samples])\n",
    "    print('clean acc ', acc)\n"
   ]
  },
  {
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   "id": "319fc9fd-4c3d-425d-ae48-948e9989b3fd",
   "metadata": {},
   "outputs": [
    {
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     },
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    },
    {
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     },
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    },
    {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean acc  0.72\n"
     ]
    }
   ],
   "source": [
    "train_simple_custom__model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "184d9764-bd6a-4d53-a0b5-4ebf05b00b9a",
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